to incorporate prior knowledge into the derivation of canonical correlation analysis.

to previous work where prior knowledge is introduced in the off-the-shelf embeddings as a post-processing step Rothe and Schütze, 2015), our approach introduces prior knowledge in the CCA derivation itself.


Tiedemann 2015shows that omitting fine-grained POS tags significantly hurts the performance of a dependency parser.

those fine-grained POS tagsets are defined monolingually and are only available for a subset of the languages with universal dependency treebanks.


The model discussed thus far conditions on the POS tags of words in the input sentence.

gold POS tags may not be available in real applications (e.g., parsing the web).


In Table 3, we assume that both gold language ID of the input language and gold POS tags are given at test time.

this assumption is not realistic in practical applications.


In this work we use the Long Short Term Memory (LSTM) variant (Hochreiter and Schmidhuber, 1997) which is shown to be a very capable sequence learner.

our algorithm and encoding method do not rely on any specific property of the LSTM architecture, and the LSTM can be transparently switched for any other RNN variant.


Thus, we are most interested in the random initialization setup: what can the network learn from the training corpus alone, without relying on external resources.

the ability to perform semi-supervised learning by initializing the word-embeddings with vectors that are pre-trained on large amount of unannotated data is an appealing property of the neuralnetwork approaches, and we evaluate our parser also in this semi-supervised setup.


In cases the tree is fully specified in advance, the relation of each word to its head can be added to the word representations v i .

in the context of parsing, the labels become known only when the modifier is attached to its parent.


This window approach is also used in the Easy-First parser of Goldberg and Elhadad (Goldberg and Elhadad, 2010) and works that extend it (Tratz and Hovy, 2011;Ma et al., 2012;Ma et al., 2013).

unlike the previous work, which made use of extensive feature engineering and rich feature functions aiming at extracting the many relevant linguistic sub-structures from the 6 subtrees and their interactions, we provide the scoring function solely with the vector-encoding of the 6 subtrees in the window.


Thus far, quantitative evidence for effective conversation strategies has been scarce, since most studies on counseling have been limited to very small sample sizes and qualitative observations (e.g., Labov and Fanshel, (1977); Haberstroh et al., (2007)).

recent advances in technology-mediated counseling conducted online or through texting (Haberstroh et al., 2007) have allowed counseling services to scale with increasing demands and to collect large-scale data on counseling conversations and their outcomes.


It is challenging to measure ambiguity and reactions to ambiguity at arbitrary points throughout the conversation since it strongly depends on the context of the entire conversation (i.e., all earlier messages and questions).

we can study nearly identi-cal beginnings of conversations where we can directly compare how more successful and less successful counselors react given nearly identical situations (the texter first sharing their reason for texting in).


Figure 7d reports the query runtimes, showing that KenLM becomes substantially slower with increasing dataset size and increasing language model order.

the runtime of our CST approach is much less affected by data size or model order.


There may be more adventurous ways to manufacture synthetic languages (see §8 for some options).

we emphasize that our current method is designed to produce fairly realistic languages.


One might forgive a TTS system that reads 9801 with the correct numeric value but in the wrong case form: a listener would likely notice the error but would usually not be misled about the message being conveyed.

reading it as nine thousand seven hundred and one is completely unacceptable, as this would actively mislead the listener.


The size of V varied by language, with the smallest, English, consisting of roughly 8,000 states and arcs, and the largest, Russian, measuring roughly 80,000 states and arcs and comprising approximately a megabyte of uncompressed binary data.

no LM was required for either English or Khmer as both have a functional L. the Georgian and Russian L are both ambiguous, so the best path through the output lattice was selected according to a trigram language model with Witten-Bell smoothing.


In this case, the ordinal number verbalizer can be constructed by applying a trivial transduction to the cardinal number verbalizer.

it is an open question whether this is a universal or whether there may be some languages in which the discrepancy is much greater, so that separate methods are necessary to construct the ordinal verbalizer.


As Figure 2b shows, a last intervening noun of the same number as the subject increased error rates only moderately, from 0.4% to 0.7% in singular subjects and from 1% to 1.4% in plural subjects.

when the last intervening noun was an agreement attractor, error rates increased by almost an order of magnitude (to 6.5% and 5.4% respectively).


Taxonomies, which serve as backbones for structured knowledge, are use-ful for many NLP applications such as question answering (Harabagiu et al., 2003) and document clustering (Fodeh et al., 2011).

handcrafted, well-structured taxonomies such as Word-Net (Miller, 1995), OpenCyc (Matuszek et al., 2006) and Freebase (Bollacker et al., 2008), which are publicly available, can be incomplete for new or specialized domains.


Note that the total timestamp contribution score of all evidence sentences d∈C W eb (t 1 ,t 2 ) T d (s 0 ) can be considered as the "weighted size" of C W eb (t 1 , t 2 ), i.e., we weigh each evidence sentence using Equation (1) and sum all these weights.

if we use only the "weighted size" of C W eb (t 1 , t 2 ) for the time-aware score Score T ime LSP (t 1 , t 2 ), there will be some issues.


The occurrence frequency of 'ISIS' was very low compared to 'Palestine Liberation Organization' which was mentioned over the past many years.

by using the timestamp contribution function to better profile the relevance of evidence over time, our method can recognize the recent relationship of 'terrorist group' with 'ISIS' while getting rid of the obsolete and incorrect relation with 'Palestine Liberation Organization'.


Our time-aware method was able to assign higher weights to the new taxonomic relations than the older relations due to their recent timestamps, even though the frequencies of these new relations are fewer than that of the older relations.

zhu's method and baseline method 1 were unable to recognize these new relations due to their relatively low frequencies in the corpus.


Perhaps unsurprisingly, there is no meaningful difference in the quality of the labels produced from the four visualization techniques.

simple visualizations (word list and word cloud) support a quick, first-glance understanding of topics, while more complex visualizations (network graph) take longer but reveal relationships between words.


The four visualization techniques lead to similar quality labels as rated by end users.

users label more quickly with the simple word list, yet tend to incorporate phrases and more generic terminology when using the more complex network graph.


For 'jurisdiction', the image search returns maps and law-related objects.

there were also misleading cases such as 'pitch' where the image search, whilst returning potentially useful pictures of sinusoidal graphs, was heavily contaminated by images of football pitches.


This paper represents foundational research investigating human language processing.

it also has the potential for application in assistant technology and embodied agents.


The guessing experiment provides a basis to estimate human expectation of already mentioned DRs (the number of clicks on the respective NPs in text).

we expect that script knowledge has a particularly strong influence in the case of first mentions.


For example, one can train a discriminative classifier to estimate the parameters.

we opted for a simpler approach-we set it equal to the empirical estimate of the expected feature vector x u,v on the training set: 6 where l refers to a document in the training set, t is (as before) a position in the document, h (l,t) and   d (l,t) are the history and the correct DR for this position, respectively.


These terms may be 7 The score would have been even higher, should the predicate be in the morphological form rinsing rather than rinsed.

embeddings of rinsing and rinsed would still be sufficiently close to each other for our argument to hold.


(2013) exploit rich features by extracting history information from a parser stack, which consists of a sequence of non-local constituents.

due to the incremental nature of shiftreduce parsing, the right-hand side constituents of the current word cannot be used to guide the action at each step.


The baseline only without character embeddings has relatively small influence on constituent hierarchy prediction.

the baseline only without input word windows has relatively smaller influence on constituent hierarchy prediction.


Second, spines are extracted from gold trees and used to prune the search space of parsing as hard constraints.

we use constituent hierarchies as soft features.


(2013) use neural features to rerank a sparse baseline parser; Durrett and Klein directly integrate sparse features into neural layers in a chart parser.

we integrate neural information into sparse features in the form of lookahead features.


The bound g(d + 2) over the number of possible values for {(s(σ i ), e(σ i ))} r i=1 is somewhat loose, as the set of p-structures over {1} ∪ where for example there is no i such that s(σ i ) = 1.

the bound is tight enough to give the O(d!)


One possible explanation is that the target context takes a higher weight and thus the system favors a shorter translation.

when we halve the contribution from the target context, the result completely loses its fluency by repeatedly generating the translation of "chūkǒu" (i.e., "the export of") until the generated translation reaches the maximum length.


For example, content words in the target sentence are more related to the translation adequacy, and thus should depend more on the source context.

function words in the target sentence are often more related to the translation fluency (e.g., "of" after "is fond"), and thus should depend more on the target context.


There is no annotated dataset for this problem.

due to the importance of such knowledge, oncologists have been painstakingly curating known relations from reading papers.


We included coreference and discourse relations in our document graph.

we didn't observe any significant gains, similar to the observation in Single-Sent.


The out-of-vocabulary rate is growing as the dataset shrinks, and therefore the performance of sisgand cbow necessarily degrades.

the proposed model (sisg) assigns non-trivial vectors to previously unseen words.


For instance, on the German GUR350 dataset, our model (sisg) trained on 5% of the data achieves better performance (66) than the cbow baseline trained on the full dataset (62).

on the English RW dataset, using 1% of the Wikipedia corpus we achieve a correlation coefficient of 45 which is better than the performance of cbow trained on the full dataset (43).


The optimal choice of length ranges depends on the considered task and language and should be tuned appropriately.

due to the scarcity of test data, we did not implement any proper validation procedure to automatically select the best parameters.


To get a fair average over languages, equation 5downweights the languages that have larger treebanks, yielding a weighted micro-average in which we define the weight As we report later in Table 5, even this simple supervised heuristic performs significantly better than state-of-the-art grammar induction systems.

it is not a trained heuristic: it has no free parameters that we can tune to optimize our evaluation metric.


This corresponds well to the constituent tree structure, where there is no direct association between non-leaf constituent nodes and input words.

it leaves the tree structure a degraded version of a perfect binary-branching variation of the sequence-structure LSTM, with one important source of information (i.e.


Similarly, we boost the performance of the BiConTree model when doubling the size of |h| from 75 to 150.

doubling the size of |h| from 150 to 300 empirically decreases the performance of the Con-Tree+Lex model.


In Figure 5a, "Emerges" is the syntactic head word of the whole phrase, which is consistent with Collins-style head finding.

"rare" is the head word of the phrase "something rare", which is different from the syntactic head.


Since NPYLM is a semi-Markov model as described in Section 2, a naïve approach would be to combine it with a semi-Markov CRF (Sarawagi and Cohen, 2005) as the discriminative model.

this strategy does not work well for two reasons: First, since a semi-Markov CRF is a model for transitions between segments, it cannot deal with character-level transitions and thus performs suboptimally on its own.


Since we do not know the endpoint of this word, we can obtain the probability p(z t = 1, z t+1 = 0) by marginalizing over the endpoint j (• • • means values all 0): Case 0-1: Similarly, as shown in Figure 8(c) this case means that a word of length at least 2 begins before time t and ends at time t. Therefore, we can marginalize over the start point of a possible word to obtain the marginal probability: Case 0-0: In principle, this means that a word begins before time t and ends later than (and including) time t + 1.

therefore, we can marginalize over both the start and end time of a possible word spanning [t, t+1] to obtain: in fact we can avoid this nested computation because the probability of p(z t , z t+1 ) over the possible values of z t and z t+1 must sum to 1.


Therefore, if topic distributions of paper i and j are similar and if η values of the cited paper's authors are high, the citation formation probability increases.

dissimilar or topically irrelevant pair of papers with less authoritative authors on the cited paper will be assigned with low probability of citation formation.


This problem is more noticeable in the LTAI since updating φ using equation 3 is slower than updating φ in vanilla LDA; moreover, per-author topical authority variable η is another local variable that algorithm needs to update a priori.

using the stochastic variational inference, the global parameters are updated after a small portion of local parameters are updated (Hoffman et al., 2013).


In the table, we observe that all authors with top h-indices have written at least 18 papers and earned at least 207 citations, which are the top 0.8% and 0.2% values respectively.

their authoritative topics retrieved by the LTAI do not overlap for any of the authors.


Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways.

this potential is currently limited by the relatively low accuracy of existing systems.


In WMT15 and WMT16, the neural QUETCH system (Kreutzer et al., 2015) and its ensemble with other neural models (Martins et al., 2016) were components of the winning systems.

none of these neural models managed to outperform a linear model when  of 35.27% in the WMT15 test set, far below the 40.84% score of the linear system built by the same team.


For medium/long sentences, we observe the opposite behavior (this is particularly clear in the 20-25 word range), with the APE-based system being generally better.

the combination of the two systems (FULLSTACKEDQE) manages to find a good balance between these two biases, being much closer to the true proportion of BAD labels for both shorter and longer sentences than any of the individual systems.


This ideal vision of debate and deliberation has taken an increasingly central role in modern theories of democracy (Habermas, 1984;Cohen, 1989;Rawls, 1997;Mansbridge, 2003).

empirical evidence has also led to an increasing awareness of the dangers of style and rhetoric in biasing participants towards the most skillful, charismatic, or numerous speakers (Noelle-Neumann, 1974;Sunstein, 1999).


54.9% of the topics were labeled as coherent by at least two annotators.

since topics are estimated separately for each debate, even the less coherent topics generally had readily interpretable meanings in the context of a given debate.


We find that strong topics are associated with more first person plurals, potentially an indicator of group responsibility (Wilson, 1990).

our model finds that weak topics are associated with second person pronouns, which may be arguments either attacking other discussants or addressing the audience (Simons and Jones, 2011).


One class of approach is to use existing resources, e.g., verb taxonomies, as a source of verbal relationships, e.g., (Grycner and Weikum, 2014), (Grycner et al., 2015).

the hierarchical relationship between verbs, out of context, is often unclear, and some verbs, e.g., "have", are ambiguous.


To fully realize the ambition of this metric, we would directly identify a sample of required end-task facts, e.g., by manual analysis of questions posed to the end-task system, or from logs of the interaction between the endtask system and the KB.

given the practical challenges of doing this at scale, we take a simpler approach and approximate this end-task distribution using facts extracted from an (independent) domain-specific text corpus (we call this a reference corpus).


Starting with 15.8K schemas, AMIE* canonicalized only 822 of those into 102 canonical schemas (using 822 schema mapping rules).

our method CASI canonicalized 4.2K schemas into 2.5K canonical schemas.


By discarding 98.8% of the training data polyglot SC obtains 89.8% of its average performance compared to the scenario when it has access to all the training sentences.

under the same scenario the FR w+c and FR w models only manage to preserve 85% and 77% of their original performance, respectively.


In natural language processing (NLP), social media data has brought variation to the fore, spurring the development of new computational techniques for charac-terizing variation in the lexicon (Eisenstein et al., 2010), orthography (Eisenstein, 2015), and syntax (Blodgett et al., 2016).

aside from the focused task of spelling normalization (Sproat et al., 2001;Aw et al., 2006), there have been few attempts to make NLP systems more robust to language variation across speakers or writers.


Mixture of experts and concatenation obtain slightly worse F1 scores than the baseline CNN system, but random attention performs significantly better.

to the SemEval datasets, individual users often contribute multiple reviews in the Ciao datasets (the average number of reviews from an author is 10.8; Table 7).


Supervised personalization typically requires labeled training examples for every individual user.

by leveraging the social network structure, we can obtain personalization even when labeled data is unavailable for many authors.


Both papers employ distant supervision, using emoticons to label additional instances.

emoticons may be unavailable for some authors or even for entire genres, such as reviews.


The inclusion of cross-lingual information results in shared cross-lingual vector spaces which can: a) boost performance on monolingual tasks such as word similarity (Faruqui and Dyer, 2014;Rastogi et al., 2015;Upadhyay et al., 2016); and b) support cross-lingual tasks such as bilingual lexicon induction (Mikolov et al., 2013a;Gouws et al., 2015;Duong et al., 2016), cross-lingual information retrieval (Vulić and Moens, 2015;Mitra et al., 2016), and transfer learning for resource-lean languages Guo et al., 2015).

prior work on cross-lingual word embedding has tended not to exploit pre-existing linguistic resources such as BabelNet.


13 Lower-Resource Languages The previous experiment indicates that bilingual specialization further improves the (already) high-quality estimates for highresource languages.

it does little to show how much (or if) the word vectors of lower-resource languages improve during such specialization.


We focus on accuracy in a listener task (i.e., at language understanding).

our most successful model integrates speaker and listener perspectives, combining predictions made by a system trained to understand color descriptions and one trained to produce them.


Blending with a speaker-based agent A second pragmatic listener L 1 can be formed in a similar way, analogous to l 1 in (5): We expect L 1 to be less accurate than L 0 or L 2 , because it is performing a listener task using only the outputs of a model trained for a speaker task.

this difference in training objective can also give the model strengths that complement those of the two listener-based agents.


The L 0 model correctly interprets all of these constraints: it gives higher probability to lowsaturation colors and greens, while avoiding bluer colors.

the result is a probability distribution nearly centered at distractor 2, the brighter green.


Recently in Lee et al., (2016) a CNNbased character-level encoder was proposed which is shared across multiple source languages.

this approach can only perform translations into a single target language.


In the "From-Scratch" column, we see that training a new model from scratch improves the zero-shot translation directions further.

this strategy has a slightly negative effect on the En↔X directions because our oversampling strategy will reduce the frequency of the data from these directions.


scores 0.65 versus 0.70 on English, bridging the gap significantly.

the high accuracy for morphologically complex languages such as Turkish and German suggests that unsupervised root detection remains a hard task.


In addition, binarization must be applied to trees, as shown in Figure 1(b), to ensure a constant number of actions (Sagae and Lavie, 2005), and to take advantage of lexical head information (Collins, 2003).

such binarization requires a set of language-specific rules, which hampers adaptation of parsing to other languages.


This is likely because the bottomup parser takes advantages of rich local features from partially-built trees, which are useful for parsing short sentences.

these local structures are can be insufficient for parsing long sentences due to error propagation.


For example, given the Sentence #2006, the bottom-up and the in-order parsers give both correct results.

the top-down parser makes an incorrect decision to generate an S, leading to subsequent incorrect decisions on VP to complete S. Sentence pattern ambiguity allows topdown guidance to over-parsing the sentence by recognizing the word "Plans" as a verb, while more bottom-up information is useful for the local disambiguation.


1993) have a core projective tree structure and trace edges that represent control structures, wh-movement and more.

most parsers and the standard evaluation metric ignore these edges and all null elements.


We present a new model for acquiring comprehensive multiword lexicons from large corpora based on competition among n-gram candidates.

to the standard approach of simple ranking by association measure, in our model n-grams are arranged in a lattice structure based on subsumption and overlap relationships, with nodes inhibiting other nodes in their vicinity when they are selected as a lexical item.


Indeed, in most experimental NLP papers, several algorithms are compared on a number of datasets where the performance of each algorithm is reported together with per-dataset statistical significance figures.

with the growing number of evaluation datasets, it becomes more challenging to draw comprehensive conclusions from such comparisons.


However, withk F isher this approach does not guarantee control over type I errors.

for k Bonf erroni , the above approach comes with such guarantees, as described in the next section.


In the next step, we evaluate each candidate in a given set of phrase pairs; it is thus crucial to get a reasonably small set of phrases.

with previous work, we collect more meaningful phrases than arbitrary short n-grams, using the following formula presented by Mikolov et al.


11 We removed phrases containing tokens unseen in the in-domain monolingual data, because we are unable to compute all our features for them.

target phrases were collected from the in-domain monolingual data, including the target side of indomain parallel data.


Unsupervised or semi-supervised probabilistic word alignment models do not play a central role in neural machine translation (NMT) (Bahdanau et al., 2015;Sutskever et al., 2014;Luong et al., 2015;Chung et al., 2016).

attention models, which are crucial for high-quality NMT, have been augmented with ideas from statistical word alignment (Luong et al., 2015;Cohn et al., 2016).


We apply our method on Chinese-English, because the annotated alignment type training data is provided in this language pair.

the proposed method is potentially language-independent and can be applied to any language-pair as long as alignment type annotated data is created.


The core generative mechanisms of LDA, in particular, have inspired numerous generalizations that account for additional information, such as the authorship (Rosen-Zvi et al., 2004), document labels (McAuliffe and Blei, 2008), or hierarchical structure .

these generalizations come at the cost of increasingly elaborate and unwieldy generative assumptions.


Scriptbase (Gorinski and Lapata, 2015) is another movie database which consists of movie screenplays (without video) and has been used to generate script summaries.

to the story comprehension tasks envisaged in MovieQA and MovieDescription, we focus on a single cinematic genre (i.e., crime series), and have access to entire episodes (and their corresponding screenplays) as opposed to video-clips or DVSs for some of the data.


The extracted segments are informative, but fail to capture the negative sentiment of some segments.

mIL-NET is able to detect positive and negative snippets via individual segment polarities.


The output segmentation has been shown to aid a diverse set of applications, such as automatic speech recognition (Afify et al., 2006), keyword spotting (Narasimhan et al., 2014), machine translation (Clifton and Sarkar, 2011) and parsing (Seeker and Ç etinoglu, 2015).

to much of this prior work, we focus on supervised segmentation, i.e., we provide the model with gold segmentations during training time.


For example, the English nominalizing affix ness (red →red+ness) can be attached to just about any adjective, including novel forms.

the archaic English nominalizing affix th (dear →dear+th, heal →heal+th, steal →steal+th) does not allow us to form new words such as cheapth.


This is similar to the decomposition of a sentence into a parse tree.

it is also natural to consider the semantic compositionality of a word, i.e., how is the meaning of the word synthesized from the meaning of the individual morphemes?


This results in quick and effective generalization to all goals in the same state space.

their work stops short of exploring generalization over map layouts, which our model is designed to handle.


The third review, perhaps due to its colloquial nature, is not entirely coherent.

the model manages to link sentences 1 and 3 to sentence 2, i.e., the movie being discussed; it also relates sentence 6 to 4, both of which express highly positive sentiment.


Even worse, for more than 60% of events, the specific day at which the event happened is not mentioned.

from the world knowledge and causal relations, the reader can infer a lot of temporal information about those events and can often infer that the event happened before or after some specific point in time.


For example, in the sentence It's the [second day] date:1998-03-06 of an [offensive] beginPoint=1998-03-05... it is clear for the annotator that the offensive started on 1998-03-05.

this date is not explicitly mentioned in the text, only the date 1998-03-06 is mentioned.


Looking at average F1 as a function of the number of children (direct responses), we found that the graph-LSTM mainly benefits nodes that have a small number of children, consistent with the two underprediction scenarios hypothesized.

many underpredicted cases are not impacted, since errors due to pruning contribute to 15-40% of the underpredicted cases, depending on the subreddit (highest for politics).


In the context of this project, Regier (1996) proposed a connectionist model that learns to predict a few spatial prepositions ("above", "below", "left", "right", "in", "out", "on", and "off") from low resolution videos containing a limited set of toy objects (circle, square, etc.).

we consider an unlimited vocabulary of real-world objects, and we do not restrict to spatial prepositions but we include actions, as well.


Task   Table 2 shows that the INI and RND 12 methods perform similarly in the Original test set, arguably be-cause a large part of the learning takes place in the parameters of the layers subsequent to the embedding layer.

in the next section we show that this is no longer the case when unseen words are present.


been translated (PAST) and what parts have not (FUTURE).

it may be difficult for a single recurrent neural network (RNN) decoder to accomplish these functionalities simultaneously.


Both implement the memory with a Neural Turing Machine (Graves et al., 2014), in which the reading and writing operations are expected to erase translated contents and highlight untranslated contents.

their models lack an explicit objective to guide such intuition, which is one of the key ingredients for the success in this work.


(2014), alignment of numbers to formulas for Mitra and Baral (2016), etc).

our method can handle a more general class of problems, while training only requires problemequation pairs coupled with rate component annotations.


Next we need to combine the sub-expression (6 + 9) with the number 3.

our inference rules were designed for the combination of two numbers only.


Both of these cases lead to noisy math concept annotations.

we tested a small sample of these annotations, and found less than 5% of them to be wrong.


This is often difficult in the presence of limited data, and noisy output from NLP tools.

we learn probabilistic models for interpretable functions like coreference, hyponymy, etc., and then use declarative knowledge involving these functions to perform reasoning.


We used a beam size of 1000 in all our experiments.

we found that varying the beam size does not effect the performance significantly.


In addition, a number of methods have been proposed for expanding dictionaries using a small initial dictionary with as few as a hundred entries (Haghighi et al., 2008).

such alignments are not available for all languages and dialects, and while a small dictionary might be feasible to acquire, discovering word mappings with no prior knowledge whatsoever is valuable.


For instance, (x, liveIn, mountain) is true for many cats and caribou, but there is little tangible similarity between the two animals and it is unclear what, if anything, can be carried over from one to the other.

if we take two animals that share a 'parent' in some taxonomy (e.g., reindeer and deer), then the likelihood of knowledge transfer increases.


(5) and (6) can be used to infer uncertain triples: if every sibling ofẽ has relationship r with an entity e , we can infer for "free" that this is the case forẽ as well.

when siblings disagree in this respect, there is more uncertainty about (ẽ, r, e ) (according to (5) and (6)), making this triple a good candidate to query.


(2017) suggested to prepend a special embedding vector according to the desired target language.

to our approach, they prepend the vector to the encoder and not to the decoder.


Under the partial constituency parse shown in 1a, with a telescope forms a constituent with man, thereby providing additional information about the individual man described.

in 1b, with a telescope does not form a constituent with man, but instead provides additional information about the action described by saw a man.


That model matches, but does not beat, the performance of the full SPINN model, which is equivalent except that it is trained to parse.

our implementation of RL-SPINN outperforms Yogatama et al.


While it is unlikely that PTB grammar is strictly optimal for any task, the empirical motivations for many of its core constituent types-the noun phrase, the prepositional phrase, and so forth-are straightforward and compelling.

our best latent tree learning models do not seem able to discover these structures.


4a) sentiment words in their respective vector spaces, we can see that positive and negative words form distinct clusters which are largely separable.

if we consider Finance sentiment words in the IMDB vector space (see Fig.


The induced sentiment lexicon on its own could be applied directly in a lexicon-based method for sentiment classification of documents, and a reasonably good performance could be achieved as we will show later in Table 4.

most of the time, lexicon-based sentiment classifiers are not as effective as learning-based sentiment classifiers.


Queries could potentially have several true answers when not constrained to rely on a specific set of support documents -e.g., queries about the parent of a certain individual.

in our setup each sample has only one true answer among C q and S q .


One problem with using WIKIREADING as an extractive RC dataset is that 54.4% of the samples do not state the answer explicitly in the given article (Hewlett et al., 2016).

we observed that some of the articles accessible by following hyperlinks from the given article often state the answer, alongside other plausible candidates.


That is, they work as part of a pipeline, and either rely on the output of a previous IE step (Banko et al., 2007), or on direct human annotation (Bollacker et al., 2008) which tends to be costly and biased in coverage.

recent neural RC methods (Seo et al., 2017a;Shen et al., 2017) have demonstrated that end-to-end language understanding approaches can infer answers directly from text -sidestepping intermediate query parsing and IE steps.


(2016) have outperformed the state-ofthe-art phrase-based systems on the NEWS shared task datasets.

we focus on multilingual transliteration with the encoder-decoder architecture or its adaptations.


Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read.

existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC.


Bethard and Parker (2016) proposed an alternate scheme, Semantically Compositional Annotation of Time Expressions (SCATE), in which times are annotated as compositional time entities (Figure 1), and suggested that this should be more amenable to machine learning.

while they constructed an annotated corpus, they did not train any automatic models on it.


ISO-TimeML is used in several corpora, including the TimeBank (Pustejovsky et al., 2003b), WikiWars (Mazur and Dale, 2010), TimeN (Llorens et al., 2012), and the Temp-Eval shared tasks (Verhagen et al., 2007;Verhagen et al., 2010;UzZaman et al., 2013).

the ISO-TimeML schema has a few drawbacks.


For example, next week refers to the week following the DCT, and in such a case the value of the property INTERVAL-TYPE for the operator NEXT would be DOCTIME.

sometimes the operator is linked to an interval that serves as reference by itself, for example, "by the year 2000".


Thus their identification is a critical factor in an interval based evaluation metric.

document times appear in many different formats: "Monday, July-24, 2017", "07/24/17 09:52 AM", "08-15-17 1337 PM", etc.


This is caused by the fact  that this model fails to identify the non-operators that compound dates in unseen formats (see Section 5.3).

evaluating HeidelTime in the SCATE annotations may not be totally fair.


We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings.

to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification.


Previous approaches to obtaining such judgments include training annotators to assign scores from 1-6 (Persing and Ng, 2017), asking annotators for simple binary or three-class categories (Wei et al., 2016b), and aggregating binary votes from multiple people (Wei et al., 2016a;Tan et al., 2016).

these approaches are limited by the cost of training annotators, a highly restricted set of categories, or the need for multiple annotators per document.


A simple approach is to use a generic classifier by obtaining a single feature vector for each pair in the training and test datasets, either by concatenating the feature vectors of the items in the pair, or by computing the difference of the two feature vectors, as in SVM-Rank (Joachims, 2002).

this approach does not produce ranked lists of convincing arguments without predicting a large number of pairwise labels, nor give scores of convincingness.


If the convincingness f (x i ) is higher than the convincingness f (x j ), the preference label i j is more likely to be true.

the label also depends on the noise terms, δ i and δ j , to allow for errors caused by, for example, disagreement between human annotators.


We also tested skip-thoughts (Kiros et al., 2015) and Siamese-CBOW (Kenter et al., 2016) with GPPL on UKPConvArgStrict and UKPConvArgRank, both with MLII optimization and the median heuristic, both alone and combined with ling.

we found that mean GloVe embeddings produced substantially better performance in all tests.


In the "no cycle" scenario, arg0 is preferred to both arg1 and arg2, which is reflected in the scores predicted by PageRank and GPPL in Figure 3.

arg3 and arg4 are not connected to the rest of the graph, and PageRank and GPPL score them differently.


The shortest length-scales are for sentiment features, pointing to a possible link between argumentation quality and sentiment.

"Very-Positive" was the feature with the largest lengthscale, either because the median was a poor heuristic in this case or because the feature was uninformative, perhaps because sarcastic statements can be confused with highly positive sentiment.


We found that both SVM and GPPL falsely classified arguments that were either very short or long and complex, suggesting deeper semantic or structural understanding of the argument may be required.

sVM also made mistakes where the arguments contained few verbs.


(2011) deploy a discrete cache to store bilingual phrases from the best translation hypotheses of previous sentences.

we use a continuous cache to store bilingual representations, which are more suitable for neural machine translation models.


One can certainly construct sentences with small lexical distance that differ semantically (e.g., insertion of the word "not").

since we mine sentences from a corpus grounded in real world events, most lexically similar sentences are also semantically similar.


Figure 5 shows that NEURALEDITOR frequently generates paraphrases despite being trained on lexical similarity, and only 1% of edits are unrelated from the prototype.

sVAE often repeats sentences exactly, and when it makes an edit it is equally likely to generate unrelated sentences.


In terms of generation techniques that capture semantics, the sentence variational autoencoder (SVAE) (Bowman et al., 2016) is closest to our work in that it attempts to impose semantic structure on a latent vector space.

the SVAE's latent vector is meant to represent the entire sentence, whereas the neural editor's latent vector represents an edit.


main types: isolating, fusional, introflexive and agglutinative, based on their position along a spectrum measuring their preference on breaking up concepts in many words (on one extreme) or rather compose them into single words (on the other extreme).

even languages belonging to the same type display different out-of-vocabulary rates and type-token ratios.


As such, we hope that these evaluation datasets can help guide multilingual language modeling research across a wide spectrum of languages.

our goal now is to verify that type/token ratio and not absolute corpus size is the deciding factor when unraveling the limitations of standard LM architectures across different languages.


are acts that trigger negative reactions in drivers (Epp et al., 2014).

officers introducing themselves and explaining the reasons for the stop are important procedural justice facets that communicate fairness and respect (Ramsey and Robinson, 2015).


For instance, the President's Task Force on 21st Century Policing recommends (and some departments require) that officers identify themselves and state the reason for the stop as an important aspect of fairness.

police departments currently have no way of easily measuring how consistently such policies are carried out during traffic stops.


Due to the sensitive nature of the data, we will not be able to publicly release the raw annotations described in Section 3.4.

we will release the labeling scheme for institutional acts in traffic stops, along with the annotation manual.


Moreover, drivers' speech may also contribute to how the conversations are shaped.

since the camera is further away from the driver than the officer, and since the environment is noisy, the audio quality of drivers' speech is poor, and further work is required to extract useful information from driver's speech.


One interesting note is that in English, which is already tokenized into words and highly polysemous, utilizing multiple embeddings per word is more effective than including multiword tokens.

in the experiments on the Chinese data, increasing the lattice size of the multicharacter tokens is more important than increasing the number of embeddings per character.


In their task, two bots should find and co-locate with a specific card.

to Info-Jigsaw, their task can be completed without communication; their agents only communicate once sharing the card location.


It is of course possible to simply keep using the same neutral verbs, e.g., increase and decrease for upward and downward changes respectively, again and again, as in most existing datato-text NLG systems.

the generated text would sound much more natural if computers could use a variety of verbs suitable in the context like human authors do.


We thus compare to mSDA (Chen et al., 2012), a very effective method for cross-domain sentiment classification on Amazon reviews.

as shown in Table 1 (row 3), mSDA did not perform competitively.


The margin is naturally decreasing as more supervision is incorporated, but ADAN is still superior when adding 12,800 labeled reviews.

the DAN with translation baseline seems unable to effectively utilize the added supervision in Chinese, and the performance only starts to show a slightly increasing trend when adding 6,400 or more labeled reviews.


The standard methodology for analyzing crowdsourced data in NLP is based on majority voting (selecting the label chosen by the majority of coders) and inter-annotator coefficients of agreement, such as Cohen's κ (Artstein and Poesio, 2008).

aggregation by majority vote implicitly assumes equal expertise among the annotators.


In a similar spirit to the results obtained in the comparison against the gold standard, modeling the ability of the annotators was also found to be essential for a good predictive performance (results presented   in Table 5).

in this type of evaluation, the unpooled models can overfit, affecting their performance (e.g., a model of higher complexity like D&S, on a small dataset like WSD).


input and training data" (AI Now Institute, 2018).

none of these proposals specify how to do so.


Further, the cues are markedly gender-neutral, improving the Bias metric by 9 percentage points in the standard task formulation and to parity in the gold-two-mention case.

to surface cues, having the full candidate set is helpful: mention alignment via a non-indicated candidate successfully scores 69% of PARALLELISM predictions.


The PMB, too, is constructed using an existing semantic parser, but a part of it is completely manually checked and corrected (i.e., gold standard).

to the GMB, the PMB involves two major additions: (a) its semantics are refined by modeling tense and using semantic tagging (Bjerva et al., 2016;, and (b) the non-logical symbols of the DRSs corresponding to concepts and semantic roles are grounded in WordNet (Fellbaum, 1998) and VerbNet (Bonial et al., 2011), respectively.


Although seq2seq AMR parsers could get away with ignoring variables during training and reinstating them in a post-processing step, for DRSs this is unfeasible.

because variable names are chosen arbitrarily, they will be hard for a seq2seq model to learn.


Some examples in semantic parsing are data recombination (Jia and Liang, 2016), paraphrasing (Berant and Liang, 2014), or exploiting machinegenerated output (Konstas et al., 2017).

before we do any experiments using extra training data, we want to be sure that we can still ben- Best gold-only 75.9 2.9 72.8 2.0 + ensemble 77.9 1.8 75.1 0.9 Gold + silver 82.9 1.8 82.7 1.1 + ensemble 83.6 1.3 83.1 0.7 Table 6: F1-score and percentage of ill-formed DRSs on the test set, for the experiments with the PMB-released silver data.


R&M's network, however, was severely limited and did not generalize correctly to held-out data.

state-of-the art morphological generation networks used in NLP, built from the modern evolution of recurrent neural networks (RNNs) explored by Elman (1990) and others, solve the same problem almost perfectly (Cotterell et al., 2016).


Finally, the model of Westermann and Goebel (1995) is arguably the most similar to a modern ED architecture, relying on simple recurrent networks to both encode input strings and decode output strings.

the model was intended to explicitly instantiate a dual route mechanism and contains an additional explicit memory component to memorize irregulars.


This corresponds to a uniform distribution over types because every verb has an effective frequency of 1.

the original R&M model was trained and tested (data was not held out) on a set of 506 stem/past pairs derived from Kučera and Francis (1967).


The non-neural MGL baseline unsurprisingly learns the regular pasttense pattern nearly perfectly, given that it is imbued with knowledge of phonological features as well as a list of phonologically illegal phoneme sequences to avoid in its output.

in our testing of the MGL, the preferred past-tense output for all verbs was never an irregular formulation.


One might say that the MGL is only intended to account for the regular route of a dual route system.

the intended scope of the MGL seems to be wider.


† indicates that a neural model's performance was found to be significantly different (p < 0.05) from the MGL.

the ED model, despite no builtin knowledge of phonology, successfully learns to conjugate nearly all the verbs in the training data, including irregulars-no reduction in scope is needed.


Although most held-out irregulars are regularized, as expected, the ED model does, perhaps surprisingly, correctly conjugate a handful of irregular forms it has not seen during training-five in the test set.

three of these are prefixed versions of irregulars that exist in the training set (retell → retold, partake → partook, withdraw  Figure 1: Single-task vs. multi-task.


Irregulars were initially produced correctly, followed by a period of overregularization preceding a final correct stage.

p&p point out that R&M only achieve this pattern by manipulating the input distribution fed into their network.


Ideally we would have sampled these synthetic treebanks from a careful estimateD of D: for example, the mean of a Bayesian posterior for D, derived from prior assumptions and UD evidence.

such adventurous "extrapolation" of unseen languages would have required actually constructing such an estimateD-which would embody a distribution over semantic content and a full theory of universal grammar!


2017create an unsupervised dependency parser that is formally similar to ours in that it uses cross-linguistic knowledge as well as statistics computed from a corpus of POS sequences in the target language.

its cross-linguistic knowledge is hand-coded: namely, the set of POS-to-POS dependencies that are allowed by the UD annotation scheme, and the typical directions for some of these dependencies.


By gradually increasing the proportion of GD languages (reducing β from §8.2), the baseline UAS increases dramatically from 63.95 to 67.97.

if all languages are uniformly sampled (β = 16 4624+16 ≈ 0.003) or only synthetic languages are used (β = 0), the UAS falls back slightly to 67.42 or 67.36.


This strategy is helpful in practice to use richer representations for some NLP problems.

such a clean modular separation of representations computed with and without attention is harder to realize in attention-based RNNs.


For example, a successful support of the claim "Weekly Idol has a host born in the year 1978" requires the information composition from three evidence sentences, two from the wiki article "Weekly Idol," and one from "Jeong Hyeong-don."

only one of them is retrieved in the top-5 candidates.


propose Tree-Node-Fix (TNF), an approximation method that scales better by additionally assuming the entailment graphs are "forest reducible," where a predicate cannot entail two (or more) predicates j and k such that neither j→k nor k→j (FRG assumption).

the FRG assumption is not correct for many real-world domains.


This includes a broader range of entailment relations such as lexical entailment.

to our method, these works rely on supervised data and take a local learning approach.


Additionally, we tried TransE (Bordes et al., 2013), another link prediction method that, despite its simplicity, produces very competitive results in knowledge base completion.

we do not present its full results, as they were worse than ConvE.


Despite the noise, untyped models for all three similarity measures still perform better than the typed ones in terms of AUC.

they do worse in the high-precision range.


We attribute this outcome to the fact that a) while entailment relations are directional, these methods are symmetric; b) the learned embeddings are optimized for tasks other than entailment or paraphrase detection; and c) the embeddings  are learned regardless of argument types.

even the BInc_untyped baseline outperforms ConvE, showing that it is important to use a directional measure that directly models entailment.


Because the system currently does not use a parser, we anticipate that adding a parser will improve performance.

because of Russian's free word order, there are more options for the location of the subject.


It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models.

little work has been done on leveraging semantics for neural machine translation (NMT).


In particular, 8,000 and 16,000 BPE merges are used on the News Commentary v11 subset and the full training set, respectively.

jAMR 6 (Flanigan et al., 2016) is adopted to parse the English sentences into AMRs before BPE is applied.


On a different but related note, our preliminary experiments on English and Hebrew show that the Arc ZEager variant always outperforms Arc Eager.

the question which of the two, Arc-Standard or Arc-ZEager, will be more suited for parsing Hebrew, remains open for our empirical investigation in Section 3.


(2013) allows for words to have vector representations, such that syntactic and semantic similarities are embodied in the vector space.

these kinds of architectures are not immediately applicable to parsing Hebrew and other MRLs.


This improved agreement prediction accuracy, and the effect persisted over the course of the sentence, indicating that this information has an effect on the model.

they did not report the effect on overall model quality, for example by measuring perplexity.


Clearly, there is room for more challenge sets in non-English languages.

perhaps more pressing is the need for large-scale non-English datasets (besides MT) to develop neural models for popular NLP tasks.


We observe a small drop in F-measure on English (−0.1) and on the Tiger corpus (−0.4) but no effect on the Negra corpus.

the resulting parser still outperforms ML-GAP-LEX, with the exception of English.


More recently,  presented an asynchronous bidirectional decoding algorithm for NMT, which extended the conventional encoder-decoder framework by utilizing a backward decoder.

these methods are more complicated than the conventional NMT framework because they require two NMT models or decoders.


We conduct analyses on Chinese-English translation to better understand our model from different perspectives.

parameters and Speeds to the standard Transformer, our model does not increase any parameters except for a hyper-parameter λ, as shown in Table 6.


The most relevant models in future modeling are twin networks (Serdyuk et al., 2018), which encourage the hidden state of the forward network to be close to that of the backward network used to predict the same token.

they still used two decoders, and the backward network contributes nothing during inference.


A GPA-based method (Kementchedjhieva et al., 2018) may also be used to jointly transform multiple languages to a common latent space.

this requires an n-way dictionary to represent n languages.


RCSLS directly optimizes the CSLS similarity score (Conneau et al., 2018), which is used during retrieval stage for GeoMM, among other algorithms.

geoMM semi optimizes a simpler classification-based square loss function (see Section 3.3).


RUM consists of an update gate u ∈ R N h that has the same function as in GRU.

instead of a reset gate, the model learns the memory target τ ∈ R N h .


We conjecture that using RUM in the decoder is better because the encoder already has an attention mechanism and thus does not need much long-term memory, and would better focus on a more local context (as in LSTM).

long-term memory is crucial for the decoder as it has to generate fluent output, and the attention mechanism cannot help it (i.e., better to use RUM).


However, WAN is not able to generalize at all to unseen labels, hence the WSABIE+ and AiTextML have a clear advantage in the zero-resource setting.

our generalized input-label model, GILE-WAN, outperforms WAN even on seen labels, where our model has higher average precision by +2.02 points, better ranking loss by +43% and comparable OneErr (−3%).


In order to refer to these, we must refer to the associations to TBUs.

as A is a relation, this requires introducing new variables, which is impossible to do without introducing quantifiers.


The authors showed that their pointwise ranking model performs well on data sets by Jeong and Titov (2010).

to our method, the ranking algorithm strictly requires a given ground truth number of segments for each document and no topic labels are predicted.


Thus, we aim to predict coherent sections with respect to document context: We approach two variations of this task: For WIKISECTION-topics, we choose a single topic label y j ∈ Y out of a small number of normalized topic labels.

from this simplified classification task arises an entailment problem, because topics might be hierarchically structured.


We clearly see from NL predictions (left side of Figure 5) that SECTOR produces coherent results with sentence granularity, with topics emerging and disappearing over the course of a document.

pV predictions are scattered across the document.


Both models successfully classify first (symptoms) and last sections (epidemiology).

only SECTOR can capture diagnosis, prevention, and treatment.


This  inference rule, manually derived, can be written out in a program form, which on execution will give the final answer.

the task of CIPITR is to actually learn the program by looking at training examples of the query and corresponding answer.


For example, for the Logical question, it invokes a gen − set with a wrong argument type None and for the Quantitative count question, it invokes the set − union operator on a non-set argument.

cIPITR, by design, can never generate a syntactically incorrect program because at every step it implicitly samples only feasible actions.


As shown in Table 5, CIPITR is able to generate at least meaningful programs having the desired answer-type or without repeating lines of code.

the NSMgenerated programs are often semantically wrong, for instance, both in the Quantitative and Quantitative Count based questions, the type of the answer is itself wrong, rendering the program meaningless.


Indeed, these methods theoretically make use of combined features from both textual and categorical features, which make them more powerful than disconnected features.

metadata is generated for human understanding, and thus we claim that these categories need to be carefully represented for machine use to Figure 1: A high-level framework of models for the Customized Text Classification Task that inputs a text with n tokens (e.g., review) and m categories (e.g., users, products) and outputs a class (e.g., positive/negative).


Previous models either combine user/product information into a single attention vector (Chen et al., 2016) or entirely separate them into distinct user and product attention vectors (Amplayo et al., 2018a).

our model creates a single attention vector, but through the k basis attention vectors, which are vectors containing fuzzy semantics among users and products.


We see that the vector space of Customized BiLSTM looks random, with very few noticeable clusters, even when we iterate with four epochs.

the basis-customized vector space starts as a cluster of one continuous spiral line, then starts to break down into smaller clusters.


This means that the model is heavily reliant on large amounts of data for optimization.

because basis customization can learn the optimal weights of category vectors more intelligently, it improves the performance of the model across all ranges of review frequency.


This showcases the importance of utilizing bipolar supervision and it constitutes an important finding compared to previous approaches Misra et al., 2018), where the feedback is defined to lie in the range of [0, 1].

mRT NEG still falls short of RAmP by 3.1 points in F1 score.


Compared with the RAMP objectives, the decrease for MRT δ 1 is smaller.

mRT δ 2 , which incorporates bipolar supervision, produces a nominal improvement over the mLE baseline.


The best-performing system achieves an F1 score of 65.4%.

humans achieve 88.8% F1, 23.4% F1 higher, indicating that there is a considerable room for improvement.


Because their answers are limited to spans, they cannot handle questions whose answers do not overlap with the passage (e.g., Q 3 , Q 4 , and Q 5 in Figure 1).

this limitation makes them more effective learners than conversational models, which have to generate an answer from a large space of pre-defined vocabulary.


PGNet alleviates the frequent response problem by focusing on the vocabulary in the passage and it outperforms seq2seq by 17.8 points.

it still lags behind DrQA by 8.5 points.


Another common knowledge source is machine-friendly databases, which organize world facts in the form of a table or a graph (Berant et al., 2013;Pasupat and Liang, 2015;Bordes et al., 2015;Saha et al., 2018;Talmor and Berant, 2018).

understanding their structure requires expertise, making it challenging to crowd-source large QA datasets without relying on templates.


It has long been pointed out that the one-topic-per-message assumption (each message contains only one topic) helps topic models alleviate the data sparsity issue in short texts on microblogs (Zhao et al., 2011;Quan et al., 2015;Nguyen et al., 2015;Li et al., 2018).

we observe contradictory results because both LF-DMM and Li et al.


We run our generic tabular algorithm for the grammar in Table 4, for input p k+1 s m , to obtain σ 1 = W .

the scores are obtained by translating indices of the shift, which pushes an element on top of a k , implies that a k will obtain right children before it can obtain left children.


The output from GCN+RC does not even appear to be grammatically correct.


Intuitively, neural networks should be able to learn rich representations by stacking a large number of layers.

empirical results often do not support such an intuition-useful information captured in earlier layers may get lost after passing through subsequent layers.


One natural question we would like to ask is how to make use of the proposed framework to perform improved graph representation learning for various graph related tasks (Xu et al., 2018).

we would also like to investigate how other NLP applications such as relation extraction (Zhang et al., 2018b) and semantic role labeling  can potentially benefit from our proposed approach.


Such a model does not rely on intermediate ASR output and is therefore not subject to error propagation.

the transformation from source speech inputs to target text outputs is much more complex than that of an ASR or MT system taken individually, which may cause the model to require more data to perform well.


The number of distinct inflected forms of each word indicates the number of morphosyntactic distinctions that the language makes on the surface.

this gives only a partial picture of complexity (Sagot, 2013).


Our initial idea was to define i-complexity as bits per form, H(M ) / |s|, where |s| is the enumerative complexity-the number of distinct forms in the paradigm.

h(M ) reflects not only the language's morphological complexity, but also its 8 Where the weight of the tree is taken to include the weight of the special edge empty → σ to the root node σ.


Note that our goal is not to advocate for one of these analyses, but merely to suggest that Ackerman and Malouf (2013)'s quantity is analysis-dependent.

9 our 9 Bonami and Beniamine (2016) have made a similar point (Rob Malouf, personal communication).


Dependency Embeddings An obvious way to take advantage of the dependency label predictions (see Section 2.1) would be to use the embedding e l of the label l with the highest score.

this would place too much emphasis on high confidence labels, which can be noisy.


Both models are worse at predicting the semantic roles of nominal predicates (compared with verbal ones).

our model is generally more accurate, especially for nominal predicates, bringing an overall improvement of 1.3 percentage point in F 1 .


Long-range dependencies often pose problems for SRL models; in fact, special networks like GCN  and PathLSTM (Roth and Lapata, 2016) have been proposed to explicitly percolate information from each word in the sentence to its syntactic neighbors.

pathLSTM performs worse than a vanilla BiLSTM model in the case of longdistance arguments, and the performance of an SRL model augmented with GCN also decreases when more than one GCN layer is stacked.


Previous proposals for incorporating syntactic information include the use of low-rank tensor factorizations (Lei et al., 2015), convolu-tional and time-domain neural networks (Foland and Martin, 2015), jointly embedded arguments and semantic roles in a shared vector space (FitzGerald et al., 2015), learning representations of shortest dependency paths between a predicate and its potential arguments (Roth and Lapata, 2016), encoding sentences with graph convolutional networks , constrained decoding (He et al., 2017), and argument pruning (He et al., 2018).

to these approaches, we do not use an external dependency parser but rather incorporate syntactic information as part of the model's learning objective.


Normally we expect most probabilities to be near 0 or 1, making the PFST nearly deterministic (i.e., close to a subsequential FST).

permitting low-probability choices remains useful to account for typographical errors, dialectal differences, and free variation in the training corpus.


7 In retrospect, there was no good reason to square the ET 0 [c(T 0 )] term.

when we started redoing the experiments, we found the results essentially unchanged.


9 Whereas the inside algorithm for (1) must consider O(n 2 ) possible constituents ofx and O(n) ways of building each, our algorithm for (2) only needs to iterate over the O(n) true constituents of T and the 1 true way of building each.

it must still consider the |W d | puncteme pairs for each constituent.


As shown in Table 5, our method achieves better performance than the punctuation restoration baselines (which ignore input punctuation).

it is soundly beaten by a new BiLSTM-CRF that we trained specifically for the task of punctuation correction.


For words not in the training data WMask is reduced, like FullLex, to the Matrix model.

to the FullLex model, which uses crosswise transformations of the input vectors, A v u and A u v, Dima (2015) uses direct transformations: u, the vector of the first word, is transformed via element-wise multiplication with its first position mask, u m , u u m .


The extracted subset contains 89.0M sentences and 2.2B tokens.

to Dutch and German, we train the word representations on word forms, due to the large number of words that were assigned an unknown lemma.


The Matrix model clearly outperforms the additive models.

its results are modest in comparison with models like WMask+, BiLinear, FullLex+, and TransWeight.


The use of data sets with a range of different sizes revealed that data sets with a smaller number of phrases per unique word can be successfully modelled using only transformation vectors.

data sets with a larger number of phrases per word require the use of transformation matrices in order to generalize.


The results in Section 5.2 have shown that the transformations-based generalization strategy used by TransWeight works well across different languages and phrase types.

understanding what the transformations encode requires taking a step back and contemplating again the architecture of the model.


Jia and Liang (2017) showed that systems trained on SQuAD could be easily fooled by the insertion of distractor sentences that should not change the answer, and SQuAD 2.0 introduces questions that are designed to be unanswerable.

we argue that questions written to be unanswerable can be identified as such with little reasoning, in contrast to NQ's task of deciding whether a paragraph contains all of the evidence required to answer a real question.


8 As stated earlier in this paper, we did instruct annotators to select the earliest instance of an answer when there are multiple answer instances on the page.

there are still cases where different annotators disagree on whether an answer earlier in the page is sufficient in comparison to a later answer, leading to differences between annotators.


We have shown that contextualized word representations perform generally better than static word embeddings on tasks related to lexical composition.

although they are on par with human performance in recognizing meaning shift, they are still far from that in revealing implicit meaning.


1 We still follow the premise of words being the core components for a neural network that deals with text.

in order to avoid the aforementioned domain dependency issue and adapt the architecture to the readability task, we inform our model with part of speech (POS) and morphological tags.


Informing the model with length distribution of texts in each reading level could lead to performance improvement in an experimental setting.

doing so would not allow us to estimate model performance in a real scenario, where text length will rarely follow the distribution seen in training sets.


Post-annotation, we ignore the free text entry for OTHER and unify OTHER and UNKNOWN into a single category.

our data collection process (and the corpus we distribute) contain this information, allowing for more complex task definitions in future work.


When using only the last debate's features and ignoring all previous debates, our performance is initially very good in the years 2013 and 2014 (when our training sets are smaller and histories shorter).

as debate histories become richer in the later years, lastonly's performance drops in comparison to that of the growing weight aggregation.


The best debaters have a higher feature count in every case except for the Flesch reading ease score.

the best users do seem to improve over time in length, H∧FW, use of emotional cues, and link use, which mostly correlates with our feature ablation in Figure 6 (p < 0.05 after Bonferroni correction).


This suggests that many of the original FCE corrections are included in GMEG-Data.

the original Wiki annotations have much lower precision and recall (40.9 and 33.2, respectively).


Prior work in GEC has focused almost exclusively on correcting errors by ELL writers, which has led to systems optimized for that demographic and metrics optimized to the few data sets available.

eLL writers are just one of the many potential beneficiaries of GeC feedback.


The n-gram baseline performs similarly to the CNLM when there is no intervening material, which is expected, as a noun will often be preceded by its article in the corpus.

its accuracy drops to chance level (0.33) in the presence of an adjective, whereas the CNLM is still able to track agreement.


In all languages, most peaks in activation mark word boundaries.


The encodings of the utterances and memory elements are then suitably combined using an attention network and fed to the decoder to generate the response, one word at a time.

these methods do not exploit the structure in the knowledge base as defined by entity-entity relations and the structure in the utterances as defined by a dependency parse.


(2018) used memory networks on top of the RNN decoder to tightly integrate KB entities with the decoder in order to generate more informative responses.

as opposed to our work, all these works ignore the underlying structure of the entityentity graph of the KB and the syntactic structure of the utterances.


For the dialogue history and query encoder, we used the dependency parse tree for capturing structural information in the encodings.

if the conversations occur in a language for which no dependency parsers exist, for example: codemixed languages like Hinglish (Hindi-English) (Banerjee et al., 2018), then we need an alternate way of extracting a graph structure from the utterances.


It is even possible to mix multiple languages in one sentence.

with the well-established evaluation frameworks for English sentence representations (Conneau et al., 2017;Wang et al., 2018) (Devlin et al., 2019) are extracted from its GitHub README.


Moreover, our transfer results are strong and homogeneous across all languages: For 11 of them, the zero-short performance  is (at most) 5% lower than the one on English, including distant languages like Arabic, Chinese, and Vietnamese, and we also achieve remarkable good results on low-resource languages like Swahili.

bERT achieves excellent results on English, outperforming our system by 7.5 points, but its transfer performance is much weaker.


For a model trained on the same corpus (Grave et al., 2016), the adaptive softmax still amounts to 240 million parameters whereas the sequence encoder has only around 50 million parameters.


It shows satisfying performance on SST-5, NER, and SRL.

it lags behind on Coref and even fails to outperform FASTTEXT cc on SNLI.


There are likely biases in CoLA that other annotation schemes could detect.

it is open to debate what a balanced data set for acceptability judgments should look like.


The pooling classifier with GloVe embeddings achieves near perfect correlation, suggesting that it systematically uses gross word order.

the remaining tests yield much poorer performance.


K-best lists can be extracted by extensions of the argmax inference algorithm for many models: the K-best Viterbi algorithm (Golod, 2009) for Hidden Markov Models (Rabiner, 1989) and Conditional Random Fields (Lafferty et al., 2001), K-best Maximum Spanning Tree (MST) algorithms for graph-based dependency parsing (Camerini et al., 1980;Hall, 2007), and so forth.

the members of K-best lists are typically quite similar to each other and do not substantially deviate from the argmax solution of the model.


(2016), which was aimed at distilling a final solution from an ensemble of parsers.

this MOM approach can naturally be extended beyond single variable potentials.


Our main results were achieved with gold POS tags.

in low-resource setups gold POS tags may be unavailable.


Particularly, the averaged UAS of the final solution of our model is 29.8, compared to 26.7 for K-best and 28.1 for 1-best.

the quality of the perturbated list is much higher than that of the K-best list, as is indicated, for example, in the gap between their best oracle solutions (46 vs. 37.6).


As for decoding, we directly follow Algorithm 1 to sample or decode greedily from the proposed model.

in practice beam-search is important to explore the output space for neural autoregressive models.


For instance, the model generates tokens approximately following the dependency parse when we used the SYN order for the machine translation task.

the model trained using the RF order learns to first produce verbs and nouns first, before filling up the sequence with remaining functional words.


As a result, their model needs to re-encode the partial sequence at every step, which is computationally more expensive.

our approach does not necessitate reencoding the entire sentence during generation.


Our model was trained with either pre-defined orders or searched adaptive orders.

to conventional neural autoregressive models that often generate from left to right, our model can flexibly generate a sequence following an arbitrary order.


Most notably, increasing the amount of context clearly increases cognitive load on annotators, and thus we would expect to see increased variance even if there were no increase in actual interpretive disagreements.

the increase in the Δ log likelihood metric is more meaningful, because randomly distributed noise (which we might expect in the case of high cognitive load/low annotator attention) should lead to higher variance but not multimodality.


Our algorithm ( § 4) follows recent work that has demonstrated the power of pre-training for improving DNN models in NLP (Peters et al., 2018;Devlin et al., 2019) and particularly for domain adaptation (Ziser and Reichart, 2018).

whereas in previous work a representation model, also known as a contextualized embedding model, is trained on a language modeling related task, our algorithm utilizes a representation model that is trained on sequence prediction tasks derived from the parser's output.


This section builds on both previous works, in that we generate an even larger dataset of over 84k sentence pairs for studying hypernymy in context, and use the existing test sets for evaluation.

unlike the previous methods, our dataset is constructed without any manual annotation or reliance on WordNet for contextual examples.


Unlike statistic POS taggers, the constraint grammar adopted by ENGCG preserves all possible POS interpretations just by dropping interpretations that are impossible in given local contexts.

the use of ENGCG does not a ect the soundness and completeness of the whole system, while it reduces signi cantly the local ambiguities that do not contribute to the whole parse.


Having obtained the set of LFG-DOP fragments in this way, each test sentence is parsed by a bottom-up chart parser using initially the indexed subtrees only.

only the Category-matching condition is enforced during the chart-parsing process.


The derivations are sampled in a topdown, leftmost order so as to maintain the LFG-DOP derivation order.

the competition sets of composable fragments are computed on the fly during the Monte Carlo sampling process by grouping the f-structure units that unify and that are coherent with the subderivation built so far.


The generic use, "nowadays", seems to have a broader scale.

terms that might point to one of these scales such as the names of days of the week, the word "year" and four digit years were also included in the training features.


Like one finds in a trade-off between precision and recall, the use of a significance test slightly improved the accuracy of the method for those pairs which it had an opinion about, but also increased the number of pairs which had to be randomly assigned an order.

the net impact of using a significance test for the BNC data was a very slight decrease in the overall prediction accuracy.


However, the pairs large, new and new, green occur fairly frequently.

in the face of this evidence we can assign this pair the order large, green , which not coincidently is the correct English word order.


Since this model was constructed using only data specifically about adjective sequences, the relative infrequency of such sequences does not degrade its performance.

while the word bigram model gave an accuracy of only 75.57%, the adjective bigram model yields an overall prediction accuracy of 88.02% for the BNC data.


By requiring the new instance to be identical to a previously seen instance in order to classify it, the direct evidence method is unable to generalize from seen pairs to unseen pairs.

to improve the method, we need a more appropriate similarity metric that allows the classifier to get information from previously seen pairs which are relevant to but not identical to new unseen pairs.


Given E, the result of the baseNP identification is assumed to be a sequence, in which some words are grouped into baseNP as follows The corresponding tag sequence is as follows: In which j i b , corresponds to the tag sequence of a baseNP: , may also be thought of as a baseNP rule.

b is a sequence of both POS tags and baseNP rules.


According to Bayes' Rule, we have ) ( We now assume that the words in E are independent.

we then use a trigram model as an approximation of is called lexical generation (or output) probability, and is called transition probability in Hidden Markov Model.


Compared with this rule pruning method, all baseNP rules are kept in our model.

in principle we have less likelihood of failing to recognize baseNP types As to the complexity of algorithm, our approach is determined by the Viterbi algorithm approach, or ) (n O , linear with the length.


When applied to an entity, this function returns the value of the entity with respect to that objective.

for instance, assuming the value tree shown in figure 1, we have: given someone's AMVf, it is possible to compute how valuable an entity is to that individual.


For higher positive values of k less objectives are included (only 2, when k=1), and the opposite happens for negative values (8 objectives are included, when k=-1).

by setting the constant k to different values, it is possible to control in a principled way how many objectives (i.e., pieces of evidence) are included in an argument, thus controlling the degree of conciseness of the generated arguments.


In this paper, we focus specifically on the resolution of a linguistic problem for Spanish texts, from the computational point of view: zero-pronouns in the "subject" grammatical position.

the aim of this paper is not to present a new theory regarding zeropronouns, but to show that other algorithms, which have been previously applied to the computational resolution of other kinds of pronoun, can also be applied to resolve zeropronouns.


Hobbs and Lappin full parse the text), direct comparisons are not possible.

in order to accomplish this comparison, we have implemented some of these approaches in SUPAR.


This has the effect of growing the number of correct clusters because the proportion of correct single word clusters will increase.

for our purposes, the number of correct multiword clusters (and not just correct clusters) are an important indicator of success.


Prominence is taken to be a relational notion: a constituent labelled 's' is stronger than its sister.

if B in (1) is strong, then A must be weak.


The main purpose of the feasibility study was to demonstrate the relevance of the phonological information on syllable structure for g2p conversion.

information and probabilities derived from an alignment of grapheme and phoneme strings, i.e.


We trained the probability for every three syllable.

the syllable model can be applied to search to measure how likely the input sequence is an English word or a Chinese word.


We suspect this arises from the fact that the documents in the Usenet data were all quite similar to one another in lexical content, in contrast to the callcenter documents.

in the call-center data the document containing F will appear much more relevant than the corpus as a whole.


This relation would not become explicit unless work is done to extract it.

new discoveries can be made by analysing published literature automatically (Finn, 1998;Swanson & Smalheiser, 1997).


Furthermore, the maximal length of a < increasing chain of constraints is bounded by n 2 , where n is the number of variables.

applications of (Distr) can only be iterated a finite number of times on constraints without simple hypernormal cycles (given redundancy elimination), and it follows by induction that ϕ is satisfiable.


As a result we did annotate this type of relations, but to achieve a reasonable agreement, and to contain somehow the annotators' work, we limited the types of relations annotators were supposed to mark up, and we specified priorities.

besides identity (IDENT) we only marked up three non-identity ('bridging' (Clark, 1977)) relations, and only relations between objects.


The Pronoun Rule re ects the intuition that pronominals are felicitously used to refer to discourse-salient e n tities.

cbs are often pronominalized, or even deleted if the grammar allows it.


coherence was measured with respect to the treatment of the remaining entities in the I-containing utterances.


analysis of the MUC data, in which we detected a small number of structural errors.

the values given here are not absolute but rather provide an indication of the relative distribution of RE types.


If we manage to do without craftsmanship, we will achieve broader applicability.

automatic thesaurus construction is an important research goal for the future.


The derivational generative capacity of a formalism also describes what parts of a derived structure combine with each other.

if we consider each derivation step to correspond to a semantic dependency, then derivational generative capacity describes what other elements a semantic element may depend on.


In most TAG-based analyses, bridge verbs adjoin at S or C 0 , and raising verbs adjoin at VP or I 0 .

the derivation for a sentence like 1 John thinks that Mary seems to sleep.


The problem is not the ambiguity per se, but that the regular form TAG parser, unlike a standard TAG parser, does not always distinguish these multiple derivations, because root and foot adjunction are both performed by the same rule analogous to our Pop-push.

for a given application of this rule, it is not possible to say which tree is adjoining into which without examining the rest of the derivation.


But Chelba and Jelinek, 1998 chooses the lexical heads of the two previous constituents as determined by a shift-reduce parser, and works better than a trigram model.

the virtual grammar serves to structure the history so that the two most useful words can be cho-sen, even though the structure of the problem itself is entirely linear.


This is where the analogy with Chelba and Jelinek, 1998 breaks down.

certain possibilities which were not apparent in a PCFG framework or prohibitively complicated might become simple to implement i n a P T AG framework; we conclude by o ering two such possibilities.


That is, PTAG generates an entire elementary tree at once, conditioned on the entire elementary tree being modi ed.

dependencies that have t o be stipulated in a PCFG b y tree transformations or parser modi cations are captured for free in a PTAG model.


the continued growth of the grammar is not as rapid as Figure 5 might indicate.


However, from the linguistic viewpoint, the physical position information can hardly grasp the relations between the linguistic structures.

besides the physical position information, our research try to select the objective feature types respectively according to the exact headword information and the heuristic information of headword and modifier.


In a next step, the correct parse was indicated by a human disambiguator, according to the reading intended in Helbig and Buscha (1996).

a precise indication of correct c/f-structure pairs was possible.


In contrast with an unregistered term, common terms should be eliminated from candidate terms.

the score calculated in the step 2 should be complemented for these purposes.


For example, in a word, 'si-seu-tem' (system), the syllable 'tem' have a high probability to be a syllable of transliterated foreign word, since the consonant of 't' in the syllable 'tem' is usually not used in a pure Korean word.

the consonant information which is acquired from a corpus can be used to determine whether a syllable in the given term is likely to be the part of a foreign word or not.


However, for Chinese users who have significantly different culture and writing style, English writing is a big barrier.

building a machine-aided English writing system, which helps Chinese users not only on spelling checking and grammar checking but also on writing in the way of native-English, is a very promising task.


In what follows, we will describe how to determine the proper translation among the candidate list.

as shown in Figure 2-1, we assume that the most proper translation of PY is the English word with the highest conditional probability among all leaf nodes, that is according to Bayes' law, the conditional probability is estimated by  For simplicity, we assume that a Chinese word doesn't depends on the translation context, so we can get the following approximate equation: We can also assume that the pinyin of a Chinese word is not concerned in the corresponding English translation, namely: It is almost impossible that two Chinese words correspond to the same pinyin and the same English translation, so we can suppose that: we get the approximation of (2-3) as follows:  The intelligent recommendation works on the sentence level.


The selection well meets the common sense.

bilingual example sentences containing " " will be retrieved as well.


5 Mechanism of Semantic Diagnostic Processing In our CALL system, the students are asked to fill in the blanks for composition in the given situation and context, using words from a given list.


With our system, we w ant to determine the bene ts of applying pronominal anaphora resolution techniques to QA systems.

we apply the developed computational system, Slot Uni cation Parser for Anaphora resolution SUPAR over documents and queries Ferr andez et al., 1999.


The probability can be expressed as the measure of the generality of a bigram.

in what follows, it is denoted by P gen (w i-1 ,w i ).


We can use the Poisson distribution to estimate an answer to the question: what is the probability that a word pair occurs in a document.

we get It turns out that using Poisson distribution, we .


In fact, it is even worse than count cutoff methods.

we use the following form instead, where α is a weighting factor tuned to maximize the performance.


It is also a simpler distribution that fits empirical distributions of content words as well as non-content words.

we try to use K mixture for bigram distribution modelling.


It then results in a relatively domain-independent language model.

we call this pruning method domain subtraction based pruning.


We simply clustered the document published in the same month of the same year as a cluster.


Unlike English, where the role of suffix dominates the generation of derived-words, Bahasa Indonesia depends on both prefix and suffix to derive new words.

to stem a derived Indonesian word in order to obtain its root-word, we have to look at the presence of both prefix and suffix in that derived-word (Nazief, 1996).


As proposed in (Abberley et al., 1999;Johnson et al., 1999), we segment the audio stream into overlapping documents of a fixed duration.

of optimization, we chose a 30 second window duration with a 15 second overlap.


Although MCMC performs well, the distribution from which the sample is obtained is only an approximation of the target sampling distribution.

samples obtained from such distributions may produce some bias in sample statistics, like sampling mean.


In this way, it is proved that we get a sample from the exact target distribution and not from an approximate distribution as in MCMC algorithms (Propp and Wilson, 1996).

we hope that in samples generated with perfect sampling, statistical parameter estimators may be less biased than those generated with MCMC.


The reason for this can be found in the capability of SCFGs to model the long-term dependencies established between the different lexical units of a sentence, and the possibility to incorporate the stochastic information that allows for an adequate modeling of the variability phenomena.

sCFGs have been successfully used on limited-domain tasks of low perplexity.


The procedure used to generate the sample made use of the "diagnosis of convergence" (Neal, 1993), a method by means of which an inicial portion of each run of the Markov chain of sufficient length is discarded.

the states in the remaining portion come from the desired equilibrium distribution.


While active learning has been shown to work for a number of tasks, the majority of active learning experiments in natural language processing have been conducted using very small seed corpora and sets of unlabeled examples.

we wish to explore situations where we have, or can afford, a nonnegligible sized training corpus (such as for part-of-speech tagging) and have access to very large amounts of unlabeled data.


By increasing the pool of unlabel ed training instances for active learning, we can improve accuracy with only a fixed additional annotation cost.

it is possible to benefit from the availability of extremely large corpora without incurring the full costs of annotation, training time, and representation size.


Some evaluation data may contain anaphors which are more difficult to resolve, such as anaphors that are (slightly) ambiguous and require real-world knowledge for their resolution, or anaphors that have a high number of competing candidates, or that have their antecedents far away both in terms of sentences/clauses and in terms of number of "intervening" NPs etc.

we suggest that in addition to the evaluation results, information should be provided in the evaluation data as to how difficult the anaphors are to resolve.


In this paper we will not consider negative RCGs, since the guide construction algorithm presented is Section 3 is not valid for this class.

in the sequel, we shall assume that RCGs are PRCGs.


In that case, it is not difficult to check that the guiding language % is 7 s , and that the guide will always answer 'yes' to any question asked by the guided parser.

the time taken by the guiding parser and by the guide itself is simply wasted.


Clearly, in the general case, no definite answer can be brought to such a question, since the total parse time may depend not only on the input grammar, the (quality of) the guiding grammar (e.g., is ) not a too "large" superset of ), the guide level, but also it may depend on the parsed sentence itself.

in our opinion, only the results of practical experiments may globally decide if using a guided parser is worthwhile .


We assume that each terminal leaf v of every elementary tree schema ½ has been labeled by a pre-terminal name of the form is an optional occurrence index.

4 the association George " n-n nxn-n nn-n " means that the inflected form "George" is a noun (suffix -n) that can occur in all trees of the "n", "nxn" or "nn" families (everywhere a terminal leaf of category noun occurs).


Both are CF clauses that the guiding parser calls independently.

for a TAG, the associated guiding parser performs substitutions as would a TAG parser, while each adjunction is replaced by two independent substitutions, such that there is no guarantee that any couple of V -tree and V tree can glue together to form a valid (adjoinable) V -tree.


9 In (Sarkar, 2000), there is some evidence to indicate that in LTAG parsing the number of trees selected by the words in a sentence (a measure of the syntactic lexical ambiguity of the sentence) is a better predictor of complexity than the number of words in the sentence.

the accuracy of the tree selection process may be crucial for parsing speeds.


We also observed that for some rules a shorter context works better.

when recording contexts around positive and negative examples, we record all the contexts with length smaller or equal to the maximal length.


This implies that contextual rules should be specific to a particular pair of translations.

we train the contextual classifier for each pair of translations separately.


Furthermore, direct comparison with an electronic thesaurus like WordNet is impossible, because it is not known a priori which lexical relations in WordNet can form paraphrases.


Node-substitution identifies the leftmost nonterminal frontier node of one subtree with the root node of a second subtree (i.e., the second subtree is substituted on the leftmost nonterminal frontier node of the first subtree).

a new sentence such as Mary likes Susan can be derived by combining subtrees from this corpus: Other derivations may yield the same tree, e.g.


The probability of a parse tree T is thus the sum of the probabilities of its distinct derivations.

let t id be the i-th subtree in the derivation d that produces tree T, then the probability of T is given by the DOP1 model considers counts of subtrees of a wide range of sizes in computing the probability of a tree: everything from counts of single-level rules to counts of entire trees.


The underlying assumption is that users will signal problems as soon as they become aware of them through the feedback provided by the system.

in a sense, this second approach represents a noisy channel filtering task: the current utterance has to be decoded as signalling a problem or not.


The improvement of organizational structure is believed to be critical in the essay revision process toward overall improvement of essay quality.

it would be desirable to have a system that could indicate as feedback to students, the discourse elements in their essays.


Based on the success of Larkey's experiments, and McCallum and Nigam's findings that the multivariate Bernoulli model performs better on texts with small vocabularies, this approach would seem to be the likely choice when dealing with data sets of essay responses.

we have adopted this approach in order to build a thesis statement classifier that can select from an essay the sentence that is the most likely candidate to be labeled as thesis statement.


However, this setup enables RE, in principle, to make use of information about precisely how a previous reference to an entity has been realised.

if the first mention of an entity is as "the man", RE may decide that a pronoun, "he" is acceptable in a subsequent reference.


In this study we chose initially to look at high-level discourse segmentation phenomena rather than those discourse segments embedded deeper in the discourse.

the time points at which the assigned task topics were started served as segmentation points.


As utterance (2) expects the user to take the turn, and continue to work on the same discourse purpose, this is Case (f).

the system generates an end utterance posture shift 11% of the time.


Revising the associated language-model probabilities complicates the search procedure, but not unimaginably so.

it seems to us that it is worth finding out whether the superior parsing performance of immediate-head parsers translates into improved language models.


This parsing model assigns a probability to a parse by a topdown process of considering each constituent c in and, for each c, first guessing the pre-terminal of c, t(c) (t for "tag"), then the lexical head of c, h(c), and then the expansion of c into further constituents e(c).

the probability of a parse is given by the equation where l(c) is the label of c (e.g., whether it is a noun phrase (np), verb phrase, etc.)


In this notation the above equation takes the following form: Because this is a point of contrast with the parsers described in the previous section, note that all of the conditional distributions are conditioned on one lexical item (either i or h).

only p(h t, l, m, u, i), the distribution for the head of c, looks at two lexical items (i and h itself), and none of the distributions look at three lexical items as do the trigram distribution of Equation 4 and the previously discussed parsing language models [4,15].


To the left of M is a sequence of one or more left labels L i (c) including the special termination symbol , which indicates that there are no more symbols to the left, and similarly for the labels to the right, R i (c).

an expansion e(c) looks like: The expansion is generated by guessing first M, then in order L 1 through L m+1 (= ), and similarly for R 1 through R n+1 .